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Fuzzy Recognition by Logic-Predicate Network

机译：逻辑谓词网络模糊识别

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The paper presents a description and justification of the correctness of fuzzy recognition by a logic-predicate network. Such a network is designed to recognize complex structured objects that can be described by predicate formulas. The NP-hardness of such an object recognition requires to separate the learning process, leaving it exponentially hard, and the recognition process itself. The learning process consists in extraction of groups of features (properties of elements of an object and the relations between these elements) that are common for objects of the same class. The main result of a paper is a reconstruction of a logic-predicate recognition cell. Such a reconstruction allows to recognize objects with descriptions not isomorphic to that from a training set and to calculate a degree of coincidence between the recognized object features and the features inherent to objects from the extracted group.
机译：本文提出了逻辑谓词网络的模糊识别的正确性的描述和理由。这种网络旨在识别可以由谓词公式描述的复杂结构化物体。这种对象识别的NP硬度需要分离学习过程，使其呈指数艰难地，以及识别过程本身。学习过程包括提取一组特征组（对象的元素属性以及这些元素之间的关系），对于同一类的对象常见。纸张的主要结果是重建逻辑谓词识别单元。这样的重建允许通过从训练集中的描述识别具有描述的对象，并从训练集计算识别的对象特征与来自提取的组的对象固有的特征之间的一致性。

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